Practical guidelines for handling head and neck computed tomography artifacts for quantitative image analysis

https://doi.org/10.1016/j.compmedimag.2018.09.002Get rights and content

Highlights

  • Head and neck patients are often affected by streak and beam hardening artifacts, impacting their inclusion in studies.

  • Streak artifacts impact the majority of radiomics features’ values.

  • Contours of structures can abut bone without affecting most radiomics features’ values if needed.

  • Most features were robust with up to 50% of the original tumor volume removed.

  • More patients’ head and neck CTs can be used in radiomics studies by simply removing slices affected by streak artifacts.

Abstract

Radiomics studies have demonstrated the potential use of quantitative image features to improve prognostic stratification of patients with head and neck cancer. Imaging protocol parameters that can affect radiomics feature values have been investigated, but the effects of artifacts caused by intrinsic patient factors have not. Two such artifacts that are common in patients with head and neck cancer are streak artifacts caused by dental fillings and beam-hardening artifacts caused by bone. The purpose of this study was to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. The robustness of feature values was tested by removing slices of the gross tumor volume (GTV) on computed tomography images from 30 patients with head and neck cancer; these images did not have streak artifacts or had artifacts far from the GTV. The range of each feature value over a percentage of the GTV was compared to the inter-patient variability at full volume. To determine the effects of beam-hardening artifacts, we scanned a phantom with 5 cartridges of different materials encased in polystyrene buildup. A cylindrical hole through the cartridges contained either a rod of polylactic acid to simulate water or a rod of polyvinyl chloride to simulate bone. A region of interest was drawn in each cartridge flush with the rod. Most features were robust with up to 50% of the original GTV removed. Most feature values did not significantly differ when measured with the polylactic acid rod or the polyvinyl chloride rod. Of those that did, the size of the difference did not exceed the inter-patient standard deviation in most cases. We conclude that simply removing slices affected by streak artifacts can enable these scans to be included in radiomics studies and that contours of structures can abut bone without being affected by beam hardening if needed.

Introduction

Radiomics, the analysis of medical images on a voxel level to extract quantitative image features, has become a popular research technique in oncology. Radiomics is based on the assumption that the gene microenvironment is expressed on a macroscopic level and that this information can be extracted by analyzing voxel-level data in various ways (Lambin et al., 2012). Therefore, by extracting texture metrics from the image, information inaccessible to the human eye alone can be obtained. This additional information has been shown, for instance, to improve the ability of survival models to distinguish patients by prognosis when added to conventional prognostic factors such as age (Bogowicz et al., 2017a,b; Fave et al., 2017; Fried et al., 2016; Ou et al., 2017; Vallieres et al., 2017). Most early radiomics studies focused on lung cancer, but patients with head and neck cancer have recently become a prominent focus of radiomics studies (Bagher-Ebadian et al., 2017; Bogowicz et al., 2017a,b; Ou et al., 2017; Parmar et al., 2015b,a; Vallieres et al., 2017).

While radiomics studies have identified several imaging features that are associated with prognosis, these findings can be affected by a variety of factors. The impact of many characteristics of imaging protocols, such as voxel size, tube current, tube voltage, and kernel, has been studied thoroughly (Fave et al., 2015; Mackin et al., 2017, 2018; Shafiq-ul-Hassan et al., 2017; Zhao et al., 2014, 2016). However, the effects of factors intrinsic to the patient have not been investigated. For example, computed tomography (CT) scans of the head and neck cover the oral cavity, where many patients have metal dental fillings that cause streak artifacts. As radiomics is based on the assumption that gene expression at a microscopic level is discernible on a macroscopic level in the voxels, it is likely that measuring the radiomics features of the structures affected by a streak artifact would not provide any valuable information about that structure. Another type of artifact observed in CT scans, beam hardening, can affect images containing bone. Because there are many bones in the area of interest in head and neck examinations, this area may be particularly prone to the effects of these small artifacts. As a result, patients whose structure of interest is affected by streak or beam-hardening artifacts are often excluded from the large data sets required to achieve sufficient statistical power for radiomics studies. Therefore, finding a way to include as many patients as possible is needed.

We aimed to test the impact of these artifacts and if needed, methods for compensating for these artifacts in head and neck radiomics studies. First, we determined whether streak artifacts do in fact alter radiomics feature values, and, if so, whether the simple technique of removing the slices affected by the streak artifact produced feature values similar to those in regions unaffected by the artifact. Second, we aimed to determine whether a buffer region is needed between bone and other structures to ensure that the measured feature values are not affected by beam-hardening artifacts.

Section snippets

Impact of streak artifacts on feature values

The impact of streak artifacts on feature values was investigated using a cohort of 458 patients with head and neck squamous cell carcinoma (HNSCC). All procedures were performed in accordance with the Declaration of Helsinki on Ethical Issues with a waiver of informed consent from the Institutional Review Board at the University of Texas MD Anderson Cancer Center. Only the patients whose CT images exhibited a visible streak artifact on slices showing the gross tumor volume (GTV) were selected,

Impact of streak artifacts on feature values

On average, 3.0 cm3 of GTV had to be removed to eliminate streak artifacts (standard deviation: 4.0 cm3, range: 0.11–28 cm3). Table 1 shows the percentage of features for which the measured value in the original GTV (with artifact) and the modified GTV (without artifact) differed significantly. Only for gray-level run length matrix features preprocessed using thresholding and intensity features preprocessed using thresholding, smoothing, and 8-bit depth resampling were fewer than 70% of the

Discussion

In this study, we showed that streak artifacts affect radiomics feature values, suggesting that regions containing such artifacts should not be included in radiomics data sets. We demonstrated that a simple technique, removing the slices with the artifact, can be used to remove up to 50% of the original GTV from the ROI while retaining similar feature values. Additionally, while the presence of bone within the image can affect some feature values, the effect is typically smaller than the spread

Conclusion

We demonstrated that streak artifacts affect the measured radiomics feature values. In order to deal with this effect, we suggest simply removing the slices with the artifact. Using this method, feature values are robust when up to 50% of the original GTV is removed. Excluding patients in whom more than 50% of the GTV is affected by the artifact only causes about 3% of patients to be excluded. Additionally, we demonstrated that contours can abut bone if needed, as most features are not affected

Declarations of interest

None.

Acknowledgements

This work was supported by the National Institutes of Health [grant #: R21CA216572]. Rachel Ger is supported by the Rosalie B. Hite Graduate Fellowship in Cancer Research and the American Legion Auxiliary Fellowship in Cancer Research awarded by the MD Anderson Cancer Center UTHealth Science Center at Houston Graduate School of Biomedical Sciences. The authors would like to acknowledge the editing assistance of the Department of Scientific Publications at MD Anderson Cancer Center.

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